TWO STAGE PATCH IMAGE CRACK EXTRACTION BY DEEP LEARNING AND PROPOSAL OF CRACK RATE CALCULATION METHOD

Abstract

 Crack extraction using deep learning for patches cut out from road surface images has the problem of omission or over-extraction for areas other than general dense-graded pavement such as drainage and concrete pavement. Therefore, we proposed and verified a two-stage road surface crack extraction method that classifies road surfaces into four classes in the previous stage and extracts cracks using the results and road surface patch images in the next stage. As a result, improvements of 8.3% to 0.6% in F value and 0.14 to 0.08 in AUC ware observed compared to the case where the road surface type was not used.

 Furthermore, as a result of proposing a method for calculating the crack rate based on the mesh method of the standard pavement survey / test method manual from the crack extraction results for such patch images, the correlation with the visual analysis in the 20m unit length evaluation crack rate was 0.94.